现代时间域的光度测验收集了许多天文学对象的观察结果，大规模调查的即将到来的时代将提供更多信息。大多数对象从未接受过光谱随访，这对于瞬态尤其至关重要。超新星。在这种情况下，观察到的光曲线可以提供负担得起的替代方案。时间序列被积极用于光度分类和表征，例如峰值和光度下降估计。但是，收集的时间序列是多维的，不规则地采样，包含异常值，并且没有明确定义的系统不确定性。机器学习方法有助于以最有效的方式从可用数据中提取有用的信息。我们考虑了基于神经网络的几种光曲线近似方法：多层感知，贝叶斯神经网络以及使流量正常化，以近似单光曲线观察。使用模拟的Parperc和Real Zwicky瞬态设施数据样本的测试表明，即使很少有观察值足以拟合网络并获得比其他最新方法更好的近似质量。我们表明，这项工作中描述的方法具有比高斯流程更快的计算复杂性和更快的工作速度。我们分析了旨在填补光曲线观察中空白的近似技术的性能，并表明使用适当的技术会提高峰值发现和超新星分类的准确性。此外，研究结果是在GitHub上可用的Fulu Python库中组织的，该库可以很容易地由社区使用。

变更点检测算法的目的是定位过程的时间演变的突然变化。在本文中，我们介绍了潜在神经随机微分方程的应用，以解决变化点检测问题。我们演示了模型在一系列合成和现实世界数据集和基准测试方面的检测功能和性能。大多数研究的方案都表明，所提出的算法的表现优于最先进的算法。我们还讨论了这种方法的优势和局限性，并指示了进一步改进的方向。

由于天文学中的大数据实时处理，超新星的光度数据驱动分类成为挑战。最近的研究表明，基于各种机器学习模型的解决方案质量卓越。这些模型学会使用其光曲线作为输入来对超新星类型进行分类。预处理这些曲线是一个关键的步骤，严重影响最终质量。在本次演讲中，我们研究了多层感知器（MLP），贝叶斯神经网络（BNN）的应用，并将流动（NF）归一化为单个光曲线的观测值。我们将这些近似值用作超新星分类模型的输入，并证明所提出的方法的表现优于基于适用于ZWICKY瞬态设施的亮点的高斯工艺的最新方法。 MLP表现出与高斯工艺相似的质量和速度增加。就近似质量而言，标准化流量也超过了高斯过程。

异常检测是一项具有挑战性的任务，经常在实际上出现所有工业和科学领域，从欺诈检测和数据质量监测到寻找罕见的疾病病例和寻找新物理学。大多数传统的异常检测方法，例如单级SVM和鲁棒自动编码器，是单级分类方法，即专注于将正常数据与空间的其余部分分开。这些方法基于正常和异常类别的可分离性的假设，随后不考虑任何异常的任何可用样本。尽管如此，在实际设置中，一些异常样品通常可用;但是，通常以平衡分类任务所需的量低，并且可以总是保持可分离的假设。这导致了重要的任务 - 将已知的异常样品掺入异常检测模型的训练程序中。在这项工作中，我们提出了一种新颖的模型 - 不可知论培训程序来解决这项任务。我们将单级分类重构为二进制分类问题，与伪异常样本区分开。通过将潜在分布的尾部进入模型，从标准化流动模型的低密度区域中抽出伪异常样本。这种方法允许容易地包括已知的异常进入任意分类器的训练过程。我们展示了我们的方法在一类问题上表现出可比的性能，最重要的是，在具有可变量的已知异常的任务上实现了可比或优越的结果。

Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. In several aspects, PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. The learning procedure of PLD solves the optimization problem related to the search for rules (called probabilistic laws), which have a minimal length and relatively high probability. At inference, ensembles of these rules are used for prediction. Probabilistic laws are human-readable and PLD based models are transparent and inherently interpretable. Applications of PLD include classification/clusterization/regression tasks, as well as time series analysis/anomaly detection and adaptive (robotic) control. In this paper, we outline the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.

We study the multiclass classification problem where the features come from the mixture of time-homogeneous diffusions. Specifically, the classes are discriminated by their drift functions while the diffusion coefficient is common to all classes and unknown. In this framework, we build a plug-in classifier which relies on nonparametric estimators of the drift and diffusion functions. We first establish the consistency of our classification procedure under mild assumptions and then provide rates of cnvergence under different set of assumptions. Finally, a numerical study supports our theoretical findings.

In many real-world scenarios, the absence of external knowledge source like Wikipedia restricts question answering systems to rely on latent internal knowledge in limited dialogue data. In addition, humans often seek answers by asking several questions for more comprehensive information. As the dialog becomes more extensive, machines are challenged to refer to previous conversation rounds to answer questions. In this work, we propose to leverage latent knowledge in existing conversation logs via a neural Retrieval-Reading system, enhanced with a TFIDF-based text summarizer refining lengthy conversational history to alleviate the long context issue. Our experiments show that our Retrieval-Reading system can exploit retrieved background knowledge to generate significantly better answers. The results also indicate that our context summarizer significantly helps both the retriever and the reader by introducing more concise and less noisy contextual information.

Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its practicality for long sequences. There are existing attention variants that improve the computational efficiency, but they have limited ability to effectively compute global information. In parallel to Transformer models, state space models (SSMs) are tailored for long sequences, but they are not flexible enough to capture complicated local information. We propose SPADE, short for $\underline{\textbf{S}}$tate s$\underline{\textbf{P}}$ace $\underline{\textbf{A}}$ugmente$\underline{\textbf{D}}$ Transform$\underline{\textbf{E}}$r. Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers. The SSM augments global information, which complements the lack of long-range dependency issue in local attention methods. Experimental results on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method. To further demonstrate the scalability of SPADE, we pre-train large encoder-decoder models and present fine-tuning results on natural language understanding and natural language generation tasks.

Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data. Previous studies augmented PLMs with symbolic knowledge for different downstream NLP tasks. However, knowledge bases (KBs) utilized in these studies are usually large-scale and static, in contrast to small, domain-specific, and modifiable knowledge bases that are prominent in real-world task-oriented dialogue (TOD) systems. In this paper, we showcase the advantages of injecting domain-specific knowledge prior to fine-tuning on TOD tasks. To this end, we utilize light-weight adapters that can be easily integrated with PLMs and serve as a repository for facts learned from different KBs. To measure the efficacy of proposed knowledge injection methods, we introduce Knowledge Probing using Response Selection (KPRS) -- a probe designed specifically for TOD models. Experiments on KPRS and the response generation task show improvements of knowledge injection with adapters over strong baselines.

Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as translucent object parts, and preserve photorealistic object appearance. To support real-time rendering without compromising rendering quality, our model uses a grid of features and a small MLP decoder that is transpiled into efficient shader code with interactive framerates. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects. Thanks to the use of standard shader code rendering is portable across many existing hardware and software systems.